Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI.
Department of Urology, Kaiser Permanente, Los Angeles, CA.
Urology. 2023 Jul;177:34-40. doi: 10.1016/j.urology.2023.01.059. Epub 2023 Apr 11.
To develop and validate a model to predict whether patients undergoing ureteroscopy (URS) will receive a stent.
Using registry data obtained from the Michigan Urological Surgery Improvement Collaborative Reducing Operative Complications from Kidney Stones initiative, we identified patients undergoing URS from 2016 to 2020. We used patients' age, sex, body mass index, size and location of the largest stone, current stent in place, history of any kidney stone procedure, procedure type, and acuity to fit a multivariable logistic regression model to a derivation cohort consisting of a random two-thirds of episodes. Model discrimination and calibration were evaluated in the validation cohort. A sensitivity analysis examined urologist variation using generalized mixed-effect models.
We identified 15,048 URS procedures, of which 11,471 (76%) had ureteral stents placed. Older age, male sex, larger stone size, the largest stone being in the ureteropelvic junction, no prior stone surgery, no stent in place, a planned procedure type of laser lithotripsy, and urgent procedure were associated with a higher risk of stent placement. The model achieved an area under the receiver operating characteristic curve of 0.69 (95% CI 0.67, 0.71). Incorporating urologist-level variation improved the area under the receiver operating characteristic curve to 0.83 (95% CI 0.82, 0.84).
Using a large clinical registry, we developed a multivariable regression model to predict ureteral stent placement following URS. Though well-calibrated, the model had modest discrimination due to heterogeneity in practice patterns in stent placement across urologists.
开发并验证一种模型，以预测接受输尿管镜检查（URS）的患者是否需要留置支架。
我们使用密歇根州泌尿外科手术改进合作组织降低肾结石手术并发症倡议中获得的登记数据，确定了 2016 年至 2020 年间接受 URS 的患者。我们使用患者的年龄、性别、体重指数、最大结石的大小和位置、当前是否留置支架、任何肾结石手术史、手术类型和疾病严重程度，拟合多变量逻辑回归模型，该模型的推导队列由三分之二的随机病例组成。在验证队列中评估模型的区分度和校准度。敏感性分析使用广义混合效应模型检查泌尿科医生的差异。
我们确定了 15048 例 URS 手术，其中 11471 例（76%）放置了输尿管支架。年龄较大、男性、结石较大、最大结石位于肾盂输尿管交界处、无既往结石手术史、无支架留置、计划采用激光碎石术治疗以及紧急手术与支架放置风险较高相关。该模型的受试者工作特征曲线下面积为 0.69（95%置信区间 0.67，0.71）。纳入泌尿科医生水平的差异可将受试者工作特征曲线下面积提高至 0.83（95%置信区间 0.82，0.84）。
我们使用大型临床登记数据，开发了一种多变量回归模型，以预测 URS 后输尿管支架的放置。尽管该模型校准良好，但由于支架放置实践模式在泌尿科医生之间存在异质性，因此其区分度较低。
